Browsing by Author "Balatkan, Eren"

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    Improving regression performance on monocular 3D object detection using bin-mixing and sparse voxel data
    (IEEE, 2021) Balatkan, Eren; Kıraç, Mustafa Furkan; Computer Science; KIRAÇ, Mustafa Furkan; Balatkan, Eren
    Accurate and fast 3D object detection plays a role of paramount importance for safe and capable autonomous machines. LiDAR point cloud based methods have demonstrated impressive results, yet expensive LiDAR sensors make such approaches infeasible for wide-scale adaptation. Camera based methods on the other hand are performing sub-optimally given safety and accuracy requirements. Traditionally, camera based 3D object detection is performed by generating pseudo-LiDAR point clouds from RGB-D data and using point-cloud based models, however, irregular nature of point cloud data representation makes it challenging to exploit spatial local correlations on 3D space and point cloud based models generally suffer from this. To this end, we propose Sparse Voxel based 3D Object Detection, our approach differs from traditional approaches by converting point cloud information to sparse voxel grid and utilizing sub-manifold sparse convolutions to extract information instead of PointNet based models. Furthermore, we propose Bin-Mixing layers. Bin-Mixing replaces the output layer of a neural network and boosts performance by representing the problem of regression in a fashion that is easier for network to learn.
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    MedSpecSearch: Medical specialty search
    (Springer Nature, 2019) Şahin, Mehmet Uluç; Balatkan, Eren; Eran, Cihan; Zeydan, E.; Yeniterzi, Reyyan; Computer Science; YENİTERZİ, Reyyan; Şahin, Mehmet Uluç; Balatkan, Eren; Eran, Cihan
    MedSpecSearch (www.medspecsearch.com) is a search engine for helping users to find the relevant medical specialty for a doctor visit based on users’ description of symptoms. This system is useful for users who are not sure of which medical specialty they should consult to. Furthermore, the API of the search engine can be used as part of the online doctor appointment and medical consultation sites to route the patient or question to the right medical specialty. The system returns the top three relevant specialties when the estimated confidence score is high. Otherwise, it asks users to input more data.